We overview and discuss several methods for the Fourier analysis of symbolic data, such as DNA sequences, emphasizing their mutual connections. We consider the indicator sequence approach, the vector and the symbolic autocorrelation methods, and methods such as the spectral envelope, that for each frequency optimize the symbolic-no-numeric mapping to emphasize any periodic data features. We dis...

  In an increasing number of practical situations, the quality of a process or product can be effectively characterized and summarized by a profile. A profile is usually a functional relationship between a response variable and one or more explanatory variables which can be modeled frequently using linear or nonlinear regression models. In this paper, we study the effect of non-normality on pro...

Two methods of spectral analysis for noisy speech recognition are proposed and tested in a speaker independent word recognition experiment under an additive white Gaussian noise environment. One is Mel-frequency cepstral coefficients (MFCC) spectral analysis on the autocorrelation sequence of the speech signal and the other is MFCC spectral analysis on its double autocorrelation sequence. The w...

Various complex oscillatory processes are involved in the generation of the motor command. The temporal dynamics of these processes were studied for movement detection from single trial electroencephalogram (EEG). Autocorrelation analysis was performed on the EEG signals to find robust markers of movement detection. The evolution of the autocorrelation function was characterised via the relaxat...

Poverty mapping in developing countries has become an increasingly important tool in the search for ways to improve living standards in an economically and environmentally sustainable manner. Although the classical econometric methods provide information on the geographic distribution of poverty, they do not take into account the spatial dependence of the data and generally they do not consider...

The Binary Integer Programming problem (BIP) is a mathematical optimization problem, with linear objective function and constraints, on which the domain of all variables {0, 1}. In BIP, every variable associated determined cost coefficient. Minmax regret under interval uncertainty (M-BIP) generalization BIP in coefficient to not known advance, but are assumed be bounded by an interval. M-BIP fi...

We introduce a general class of time discounting, which includes time-inconsistent ones, into repeated games with perfect monitoring. A strategy profile is called an agent subgame perfect equilibrium if there is no profitable one-shot deviation at any history. We characterize strongly symmetric agent subgame perfect equilibria for repeated games with symmetric stage game. We find that the harsh...

The significance of land cover as an environmental variable has made land use change an important subject in global environmental change and sustainable development. Modeling land use change has attracted considerable attention. Currently, empirical estimation models using statistical techniques are one of mostly used spatial models to simulate land use pattern and its changes. Empirical estima...